Evidence map›Paper›PMID 34483714›Full record

ArticleHealth services & outcomes research methodology2021

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures.

Brian G Vegetabile, Beth Ann Griffin, Donna L Coffman, Matthew Cefalu, Michael W Robbins, Daniel F McCaffrey

Abstract read
In one paragraph

Article in Health services & outcomes research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  6. Article
  7. Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025
    Article
  8. Article
  9. Childhood PMProceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
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  16. Article
  17. Matching on Generalized Propensity Scores with Continuous Exposures.Journal of the American Statistical Association · 2024
    Article
  18. Article
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Brian G VegetabileRAND Corporation, Santa Monica, CA.
Beth Ann GriffinRAND Corporation, Santa Monica, CA.
Donna L CoffmanTemple University, Philadelphia, PA.
Matthew CefaluRAND Corporation, Santa Monica, CA.
Michael W RobbinsRAND Corporation, Pittsburgh, PA.
Daniel F McCaffreyEducational Testing Service, Princeton, NJ.

Funding

Improving Causal Inference Tools for Addiction ResearchersR01DA045049 · NIDA · RAND CORPORATION · PI GRIFFIN, BETH ANN, MCCAFFREY, DANIEL F · 2018 to 2020
$2.4M
Data Fusion for Predicting Long-term Program ImpactsR21AG058123 · NIA · RAND CORPORATION · PI ROBBINS, MICHAEL · 2018 to 2019
$381k
NIA NIH HHS R21 AG058123NIDA NIH HHS R01 DA045049
6 · The paper itself

Abstract

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.

Indexed as

causal inferencelocal linear regressionmental healthsubstance abuseweighted estimation

Identifiers

PMID34483714
PMCPMC8415174

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.